Papers by Quentin Brabant
Factual Knowledge Assessment of Language Models Using Distractors (2025.coling-main)
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Hichem Ammar Khodja, Abderrahmane Ait gueni ssaid, Frederic Bechet, Quentin Brabant, Alexis Nasr, Gwénolé Lecorvé
| Challenge: | Language models encode extensive factual knowledge within their parameters. |
| Approach: | They propose a new interpretable knowledge assessment method that leverages distractors to provide incorrect alternatives to the correct answer. |
| Outcome: | The proposed method shows that it is aligned with human judgment and stronger robustness to verbalization artifacts. |
WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models (2024.lrec-main)
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| Challenge: | Factual update is a task of inserting, replacing, or removing facts in large language models. |
| Approach: | They present a dataset that describes the evolution of factual knowledge between two dates as a collection of simple facts divided into three categories: new, obsolete, and static. |
| Outcome: | The proposed dataset compares the state of the Wikidata knowledge base at 4 January 2021 and 27 February 2023. |
KGConv, a Conversational Corpus Grounded in Wikidata (2024.lrec-main)
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| Challenge: | a large corpus of 71k English conversations contains on average 8.6 questions . Unlike open domain and task-oriented dialogues, information seeking conversations are driven by the desire to acquire or evaluate knowledge. |
| Approach: | They propose a large corpus of 71k English conversations where each question-answer pair is grounded in a Wikidata fact. |
| Outcome: | The proposed dataset can be used for knowledge-based, conversational question generation . it can also be used to generate single-turn questions from Wikidata triples, question rewriting, question answering from conversation or knowledge graphs and quiz generation. |
SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (2022.aacl-main)
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| Challenge: | a paper focuses on the generation of natural language questions based on SPARQL queries . knowledge-based approaches have become popular in the field of question answering and dialogue . |
| Approach: | This paper focuses on the generation of natural language questions based on SPARQL queries . it uses 4 knowledge-based QA corpora homogenized for the task and a new challenge set is introduced . |
| Outcome: | The proposed task is based on the generation of questions in a conversational context. |
CoQAR: Question Rewriting on CoQA (2022.lrec-1)
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| Challenge: | Existing systems that ask questions in a conversational context may have contextual dependencies that make the understanding difficult. |
| Approach: | They propose to rewrite questions into an out-of-context form to facilitate understanding . they propose to use this form to train and evaluate conversational question answering models . |
| Outcome: | The proposed model can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. |